One challenge in any introductory statistics course is helping our students understand the logic of hypothesis testing. In this webinar I'll demonstrate one of my favorite examples for doing this. The data are a sample of golfballs. The hypothesis is that the number on a golfball is equally like to be a 1, 2, 3, or 4. Using a function written in R, I allow students to design their own test statistics and then produce a graphical display of the sampling distribution and calculate empirical p-values. This activity can be used in introductory classes at all levels - even if you don't cover goodness-of-fit testing. It can be used as a first introduction to inference, as a motivation for the chi-squared test statistic, as an example of goodness of fit testing, or as a demonstration of simulation-based inference.